Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Interaction of perception and gaze control in autonomous vehicles

Abstract: For robust and secure behavior in natural environment an autonomous vehicle needs an elaborate vision sensor as main source of information The vision sensor must be adaptable to the external situation, the mission, the capabilities of the vehicle and the knowledge about the external world accumulated up to the present time In the EMS-Vision system, this vision sensor consists of four cameras with different focal lengths mounted on a highly dynamic pan-tilt camera head Image processing, gaze control and behavior decision interact with each other in a closed loop The image processing experts specify so-called regions of attention (RoAs) for each object in 3D object coordinates These RoAs should be visible with a resolution as required by the measurement techniques applied The behavior decision module specifies the relevance of obstacles like road segments, crossings or landmarks in the situation context The gaze control unit takes all this information in order to plan, optimize and perform a sequence of smooth pursuits, interrupted by saccades The sequence with the best information gain is performed The information gain depends on the relevance of objects or object parts, the duration of smooth pursuit maneuvers, the quality of perception and the number of saccades The functioning of the EMS-Vision system is demonstrated in a complex and scalable autonomous mission with the UBM test vehicle VAMORS

Intelligent Simulation in Designing Complex Dynamic Control Systems

Abstract: We develop Phase Space Navigator, an autonomous system for control synthesis of nonlinear dynamical systems The Phase Space Navigator automatically designs a controller for a nonlinear system in phase space It generates global control laws by synthesizing the desired phase-space flow “shapes” for the system and intelligently planning and navigating the system along desired control trajectories in phase space It is particularly suitable for synthesizing high-performance control systems that do not lend themselves to traditional design and analysis techniques It can also assist control engineers in exploring much larger design spaces than otherwise possible

Dynamics of beliefs

Abstract: The dynamics of beliefs is one of the major components of any autonomous system, that should be able to incorporate new pieces of information. In this paper we give a quick overview of the main operators for belief change, in particular revision, update, and merging, when the beliefs are represented in propositional logic. And we discuss some works on belief change in more expressive frameworks.

Mutual Influence-aware Runtime Learning of Self-adaptation Behavior

Abstract: Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer traditional design-time decisions to runtime and into the responsibility of systems themselves. To deal with unforeseen events and conditions, systems need creativity—typically realized by means of machine learning capabilities. Such learning mechanisms are based on different sources of knowledge. Feedback from the environment used for reinforcement purposes is probably the most prominent one within the self-adapting and self-organizing (SASO) systems community. However, the impact of other (sub-)systems on the success of the individual system’s learning performance has mostly been neglected in this context.In this article, we propose a novel methodology to identify effects of actions performed by other systems in a shared environment on the utility achievement of an autonomous system. Consider smart cameras (SC) as illustrating example: For goals such as 3D reconstruction of objects, the most promising configuration of one SC in terms of pan/tilt/zoom parameters depends largely on the configuration of other SCs in the vicinity. Since such mutual influences cannot be pre-defined for dynamic systems, they have to be learned at runtime. Furthermore, they have to be taken into consideration when self-improving their own configuration decisions based on a feedback loop concept, e.g., known from the SASO domain or the Autonomic and Organic Computing initiatives.We define a methodology to detect such influences at runtime, present an approach to consider this information in a reinforcement learning technique, and analyze the behavior in artificial as well as real-world SASO system settings.

Multi-Criteria Decision Making and Behavior Assignment in Sensor Networks

Abstract: The realm of applications for sensor networks is diverse including military, commercial and environmental monitoring. The behavior of these distributed systems of sensors is highly application-specific. The behavior of the sensing system must be tailored in order to meet the demands of the given application. Prioritizing such system behavior for a given application should greatly improve the performance of achieving the desired task. In this paper, we demonstrate the concept of multi-criteria decision making in a system-of-systems application (mobile robotic system and system of sensors) and how the decision making problem can be generalized to adapt the operational behavior of large-scale autonomous system of sensors.